-
GPU 사용가능한 AI Hub 설치
안녕하세요, 현재 AI Hub 2026.0.2 버전을 사용 중입니다. GPU를 지원하는 환경으로 업그레이드하기 위해 다음 페이지의 안내페이지를 참고하였습니다. https://docs.rapidminer.com/latest/hub/install/docker-compose/deep-learning.html nvidia-docker2까지는 설치를 완료했습니다. 하지만 안내(Altair AI Hub)의 환경 파일 예제에 표시된 버전이 너무 오래된 버전(9.9.0)입니다. 기존 .env 파일과 docker-compose.yml 파일에서 어떤 부분을 수정해야 할지 잘 모르겠습니다.…
-
GPU-enabled AI Hub install
Hi, I am currently using the 2026.0.2 version of AI Hub. To upgrade to an environment that supports GPUs, I followed the instructions on the following page: https://docs.rapidminer.com/latest/hub/install/docker-compose/deep-learning.html I have completed the installation up to nvidia-docker2. However, the version shown in…
-
Hello everyone, I would like to ask how to use GPU acceleration for training data in physicsAI?
Description: Kindly add description here Product/Topic Name : Kindly include the product name, version if applicable here. Also, Don't forget to tag with the product/topic related to your question. *Note from Community Team - Once you receive a satisfactory answer on your question, kindly mark your answers as "Accepted" so…
-
Differences Between CPU and GPU Versions of EDE
Hello everyone, I hope you are all doing well. I am planning to start a new simulation of spiral jet milling using EDEM coupled with Fluent. Due to the high computational cost associated with this work, I am considering switching from CPU-based to GPU-based computation. When I previously worked with EDEM in 2022, I recall…
-
ERROR 50163: Internal Feko error.
When I was using the GPU acceleration feature of RL-GO, I encountered ERROR 50163: Internal Feko error. My model has been meshed using flat triangles, and the rays of RL-GO have been set to the "Fixed grid increments" mode. As far as I know, these two settings meet the requirements for GPU acceleration of RL-GO. However,…
-
Utilizing RTX 3050 GPU for Polyfoam 2024 Simulation
I'm currently using Polyfoam 2024 for simulations, but it's only utilizing my CPU and the process is quite slow. I have an RTX 3050 GPU and I'd like to know how I can configure Polyfoam 2024 to use my GPU for simulations to speed up the process. I am using Student Edition License. Any guidance or steps to get this set up…
-
[EDEM] Can we get energy loss with GPU calcuration
Hi, I'd like to get energy loss after simulation by GPU. I think currrent .dill file that we use for getting energy loss is available only CPU calculation. Do you have any idea to get it with GPU calculation.
-
Explain Predictions using gpu?
Is there a way the "Explain Predictions" Operator could benefit from gpu (NVIDIA RTX A4000 with CUDA installed)?
-
UI error in RapidMiner
Hello RapidMiner community, I have faced a graphical (UI) issue when working a bit in RapidMiner, the problem is that whenever I do some tasks on RapidMiner, it shows the below like interface: and it just gets worse anytime I hover the mouse over something within the UI, however, I tried to disable the graphic card, and…
-
Backend GPU activation troubles - C++ or JavaCPP
Hello, I try to activate the GPU backend at a Windows11 AMD with RTX3060 GPU Notebook. I installed cuda 10.1 and cudnn 7.6. The cudart64_101.dll I am not sure where to place. deviceQuery.exe gives: CUDA Driver = CUDART, CUDA Driver Version = 11.4, CUDA Runtime Version = 10.1, NumDevs = 1, Device0 = NVIDIA GeForce RTX 3060…
-
Does GPU support Deeplearning Python script
If I have deep learning code written in Keras and in python, can I execute the code in GPU in enterprise edition? In some of the documentation it's mentioned GPU works only with DeepLearnig operator, does it mean it doesn't work with plain Python code for Deep learning
-
RapidMiner won't recognize my GPU?
I have an RTX 2060, and I tried changing the backend to GPU-CUDA. Rapidminer says "The Backend GPU-CUDA is not available. Please install the necessary utilities and restart rapidminer". My GPU works well with everything else, except RM. I have an educational license if that helps.
-
Running Deep Learning extension with CUDA 10.2?
I can see in the new version of the Deep Learning extension the requirement for CUDA 10.0. However the new Tensorflow, which I also use on my system, requires CUDA 10.1+ and runs with the newest one too, which is CUDA 10.2. The release notes for the extension suggest to contact RM for assistance. As it is, the preferences…
-
GPU slower than CPU
Hi, I switched Deep learning to use GPU instead of CPU(1 core), but this runs slower. I see that the GPU utilization is very less (2 to 3%) while the process is running. When I use CPU the CPU utilization is 70% approx. I am using a batch size of 32. Is it because of the smaller batch size? Thanks, Varun
